Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	14.0	33.307833
daal4py_KNeighborsClassifier	0.0	5.0	26.047819
KNeighborsClassifier_kd_tree	0.0	6.0	35.520019
daal4py_KNeighborsClassifier_kd_tree	0.0	1.0	45.018181
KMeans_tall	0.0	1.0	48.418566
daal4py_KMeans_tall	0.0	1.0	17.846428
KMeans_short	0.0	0.0	26.676209
daal4py_KMeans_short	0.0	0.0	13.397495
LogisticRegression	0.0	1.0	10.559725
daal4py_LogisticRegression	0.0	1.0	2.411753
Ridge	0.0	1.0	1.396874
daal4py_Ridge	0.0	0.0	23.457970
total	0.0	35.0	44.147974

KNeighborsClassifier vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier	fit	0.170	0.009	1000000	1000000	100	brute	-1	1	NaN	NaN	0.643	0.064	0.265	0.030	See
1	KNeighborsClassifier	predict	0.268	0.017	1000000	1	100	brute	-1	1	1.0	1.0	0.200	0.030	1.340	0.221	See
2	KNeighborsClassifier	predict	34.485	0.000	1000000	1000	100	brute	-1	1	1.0	1.0	4.416	0.115	7.809	0.203	See
3	KNeighborsClassifier	fit	0.155	0.006	1000000	1000000	100	brute	-1	5	NaN	NaN	0.661	0.052	0.234	0.021	See
4	KNeighborsClassifier	predict	0.256	0.013	1000000	1	100	brute	-1	5	1.0	1.0	0.176	0.021	1.454	0.187	See
5	KNeighborsClassifier	predict	40.868	0.000	1000000	1000	100	brute	-1	5	1.0	1.0	4.138	0.157	9.876	0.376	See
6	KNeighborsClassifier	fit	0.147	0.011	1000000	1000000	100	brute	-1	100	NaN	NaN	0.504	0.015	0.292	0.023	See
7	KNeighborsClassifier	predict	0.211	0.009	1000000	1	100	brute	-1	100	1.0	1.0	0.110	0.004	1.921	0.110	See
8	KNeighborsClassifier	predict	41.192	0.000	1000000	1000	100	brute	-1	100	1.0	1.0	4.000	0.043	10.299	0.110	See
9	KNeighborsClassifier	fit	0.129	0.004	1000000	1000000	100	brute	1	1	NaN	NaN	0.513	0.011	0.250	0.010	See
10	KNeighborsClassifier	predict	0.195	0.002	1000000	1	100	brute	1	1	1.0	1.0	0.108	0.004	1.803	0.078	See
11	KNeighborsClassifier	predict	17.570	0.654	1000000	1000	100	brute	1	1	1.0	1.0	3.882	0.029	4.526	0.172	See
12	KNeighborsClassifier	fit	0.151	0.017	1000000	1000000	100	brute	1	5	NaN	NaN	0.511	0.020	0.295	0.035	See
13	KNeighborsClassifier	predict	0.245	0.027	1000000	1	100	brute	1	5	1.0	1.0	0.098	0.002	2.498	0.281	See
14	KNeighborsClassifier	predict	25.424	0.056	1000000	1000	100	brute	1	5	1.0	1.0	3.943	0.065	6.448	0.107	See
15	KNeighborsClassifier	fit	0.129	0.003	1000000	1000000	100	brute	1	100	NaN	NaN	0.512	0.016	0.251	0.009	See
16	KNeighborsClassifier	predict	0.214	0.007	1000000	1	100	brute	1	100	1.0	1.0	0.103	0.002	2.068	0.078	See
17	KNeighborsClassifier	predict	26.165	0.428	1000000	1000	100	brute	1	100	1.0	1.0	3.983	0.069	6.569	0.156	See
18	KNeighborsClassifier	fit	0.059	0.002	1000000	1000000	2	brute	-1	1	NaN	NaN	0.104	0.004	0.567	0.029	See
19	KNeighborsClassifier	predict	0.021	0.002	1000000	1	2	brute	-1	1	1.0	1.0	0.005	0.000	4.747	0.633	See
20	KNeighborsClassifier	predict	25.565	0.008	1000000	1000	2	brute	-1	1	1.0	1.0	0.809	0.015	31.590	0.571	See
21	KNeighborsClassifier	fit	0.063	0.003	1000000	1000000	2	brute	-1	5	NaN	NaN	0.105	0.004	0.601	0.032	See
22	KNeighborsClassifier	predict	0.029	0.002	1000000	1	2	brute	-1	5	1.0	1.0	0.004	0.000	6.475	0.625	See
23	KNeighborsClassifier	predict	34.460	0.000	1000000	1000	2	brute	-1	5	1.0	1.0	0.820	0.015	42.049	0.762	See
24	KNeighborsClassifier	fit	0.062	0.003	1000000	1000000	2	brute	-1	100	NaN	NaN	0.106	0.004	0.585	0.038	See
25	KNeighborsClassifier	predict	0.029	0.002	1000000	1	2	brute	-1	100	1.0	1.0	0.005	0.000	6.277	0.714	See
26	KNeighborsClassifier	predict	33.844	0.000	1000000	1000	2	brute	-1	100	1.0	1.0	0.885	0.023	38.263	0.992	See
27	KNeighborsClassifier	fit	0.061	0.003	1000000	1000000	2	brute	1	1	NaN	NaN	0.105	0.002	0.579	0.029	See
28	KNeighborsClassifier	predict	0.015	0.001	1000000	1	2	brute	1	1	1.0	1.0	0.004	0.000	3.448	0.365	See
29	KNeighborsClassifier	predict	11.652	0.277	1000000	1000	2	brute	1	1	1.0	1.0	0.814	0.014	14.315	0.422	See
30	KNeighborsClassifier	fit	0.060	0.003	1000000	1000000	2	brute	1	5	NaN	NaN	0.104	0.002	0.577	0.034	See
31	KNeighborsClassifier	predict	0.022	0.001	1000000	1	2	brute	1	5	1.0	1.0	0.004	0.000	4.947	0.450	See
32	KNeighborsClassifier	predict	20.110	0.331	1000000	1000	2	brute	1	5	1.0	1.0	0.808	0.008	24.902	0.471	See
33	KNeighborsClassifier	fit	0.059	0.001	1000000	1000000	2	brute	1	100	NaN	NaN	0.105	0.004	0.560	0.023	See
34	KNeighborsClassifier	predict	0.022	0.001	1000000	1	2	brute	1	100	1.0	1.0	0.004	0.000	4.911	0.566	See
35	KNeighborsClassifier	predict	20.463	0.799	1000000	1000	2	brute	1	100	1.0	1.0	0.882	0.012	23.214	0.963	See

Plots

Shared hyperparameters:

	value
algorithm	brute

KNeighborsClassifier_kd_tree vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier_kd_tree	fit	2.868	0.045	1000000	1000000	10	kd_tree	-1	1	NaN	NaN	0.705	0.008	4.071	0.080	See
1	KNeighborsClassifier_kd_tree	predict	0.004	0.001	1000000	1	10	kd_tree	-1	1	1.0	1.0	0.000	0.000	9.763	5.335	See
2	KNeighborsClassifier_kd_tree	predict	0.442	0.005	1000000	1000	10	kd_tree	-1	1	1.0	1.0	0.117	0.002	3.776	0.083	See
3	KNeighborsClassifier_kd_tree	fit	2.927	0.037	1000000	1000000	10	kd_tree	-1	5	NaN	NaN	0.744	0.009	3.936	0.069	See
4	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	10	kd_tree	-1	5	1.0	1.0	0.000	0.000	8.034	3.394	See
5	KNeighborsClassifier_kd_tree	predict	0.889	0.015	1000000	1000	10	kd_tree	-1	5	1.0	1.0	0.215	0.002	4.141	0.081	See
6	KNeighborsClassifier_kd_tree	fit	2.921	0.054	1000000	1000000	10	kd_tree	-1	100	NaN	NaN	0.712	0.014	4.104	0.112	See
7	KNeighborsClassifier_kd_tree	predict	0.007	0.001	1000000	1	10	kd_tree	-1	100	1.0	1.0	0.001	0.000	10.460	3.838	See
8	KNeighborsClassifier_kd_tree	predict	2.755	0.024	1000000	1000	10	kd_tree	-1	100	1.0	1.0	0.649	0.010	4.242	0.074	See
9	KNeighborsClassifier_kd_tree	fit	2.966	0.042	1000000	1000000	10	kd_tree	1	1	NaN	NaN	0.755	0.011	3.929	0.081	See
10	KNeighborsClassifier_kd_tree	predict	0.001	0.001	1000000	1	10	kd_tree	1	1	1.0	1.0	0.000	0.000	3.156	2.113	See
11	KNeighborsClassifier_kd_tree	predict	0.806	0.013	1000000	1000	10	kd_tree	1	1	1.0	1.0	0.117	0.002	6.878	0.152	See
12	KNeighborsClassifier_kd_tree	fit	2.918	0.043	1000000	1000000	10	kd_tree	1	5	NaN	NaN	0.702	0.009	4.156	0.080	See
13	KNeighborsClassifier_kd_tree	predict	0.002	0.001	1000000	1	10	kd_tree	1	5	1.0	1.0	0.000	0.000	3.831	1.963	See
14	KNeighborsClassifier_kd_tree	predict	1.541	0.016	1000000	1000	10	kd_tree	1	5	1.0	1.0	0.215	0.003	7.169	0.117	See
15	KNeighborsClassifier_kd_tree	fit	2.919	0.048	1000000	1000000	10	kd_tree	1	100	NaN	NaN	0.758	0.011	3.849	0.084	See
16	KNeighborsClassifier_kd_tree	predict	0.004	0.001	1000000	1	10	kd_tree	1	100	1.0	1.0	0.001	0.000	4.905	2.229	See
17	KNeighborsClassifier_kd_tree	predict	5.156	0.095	1000000	1000	10	kd_tree	1	100	1.0	1.0	0.651	0.012	7.925	0.208	See
18	KNeighborsClassifier_kd_tree	fit	1.358	0.023	1000000	1000000	2	kd_tree	-1	1	NaN	NaN	0.494	0.007	2.748	0.063	See
19	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	1	1.0	1.0	0.000	0.000	7.482	4.650	See
20	KNeighborsClassifier_kd_tree	predict	0.036	0.003	1000000	1000	2	kd_tree	-1	1	1.0	1.0	0.001	0.000	39.804	18.182	See
21	KNeighborsClassifier_kd_tree	fit	1.311	0.035	1000000	1000000	2	kd_tree	-1	5	NaN	NaN	0.498	0.006	2.635	0.076	See
22	KNeighborsClassifier_kd_tree	predict	0.002	0.001	1000000	1	2	kd_tree	-1	5	1.0	1.0	0.000	0.000	14.423	7.250	See
23	KNeighborsClassifier_kd_tree	predict	0.038	0.001	1000000	1000	2	kd_tree	-1	5	1.0	1.0	0.002	0.001	22.607	8.818	See
24	KNeighborsClassifier_kd_tree	fit	1.298	0.016	1000000	1000000	2	kd_tree	-1	100	NaN	NaN	0.500	0.015	2.594	0.082	See
25	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	2	kd_tree	-1	100	1.0	1.0	0.000	0.000	12.290	6.141	See
26	KNeighborsClassifier_kd_tree	predict	0.059	0.004	1000000	1000	2	kd_tree	-1	100	1.0	1.0	0.008	0.001	7.861	1.018	See
27	KNeighborsClassifier_kd_tree	fit	1.274	0.013	1000000	1000000	2	kd_tree	1	1	NaN	NaN	0.506	0.012	2.518	0.066	See
28	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	1	1.0	1.0	0.000	0.000	4.650	2.977	See
29	KNeighborsClassifier_kd_tree	predict	0.032	0.002	1000000	1000	2	kd_tree	1	1	1.0	1.0	0.001	0.001	33.869	19.025	See
30	KNeighborsClassifier_kd_tree	fit	1.272	0.012	1000000	1000000	2	kd_tree	1	5	NaN	NaN	0.507	0.009	2.512	0.050	See
31	KNeighborsClassifier_kd_tree	predict	0.001	0.001	1000000	1	2	kd_tree	1	5	1.0	1.0	0.000	0.000	4.654	3.743	See
32	KNeighborsClassifier_kd_tree	predict	0.034	0.002	1000000	1000	2	kd_tree	1	5	1.0	1.0	0.001	0.000	28.440	6.481	See
33	KNeighborsClassifier_kd_tree	fit	1.278	0.017	1000000	1000000	2	kd_tree	1	100	NaN	NaN	0.506	0.008	2.525	0.054	See
34	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	100	1.0	1.0	0.000	0.000	4.912	2.596	See
35	KNeighborsClassifier_kd_tree	predict	0.063	0.003	1000000	1000	2	kd_tree	1	100	1.0	1.0	0.007	0.001	8.829	1.040	See

Plots

Shared hyperparameters:

	value
algorithm	kd_tree

KMeans_tall vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_tall	fit	0.638	0.007	1000000	1000000	2	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	0.321	0.008	1.989	0.052	See
1	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.018	1.152	See
2	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.943	1.006	See
3	KMeans_tall	fit	0.546	0.008	1000000	1000000	2	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	0.284	0.006	1.923	0.052	See
4	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.380	1.279	See
5	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.736	0.821	See
6	KMeans_tall	fit	7.432	0.067	1000000	1000000	100	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	3.977	0.043	1.869	0.026	See
7	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.093	1.139	See
8	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.526	1.321	See
9	KMeans_tall	fit	6.788	0.005	1000000	1000000	100	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	3.763	0.036	1.804	0.017	See
10	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.233	0.928	See
11	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.749	0.865	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

KMeans_short vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_short	fit	0.390	0.010	10000	10000	2	full	k-means++	30	300	1	0.0	26.0	NaN	30.0	NaN	0.155	0.009	2.525	0.156	See
1	KMeans_short	predict	0.000	0.000	10000	1	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	0.903	0.531	See
2	KMeans_short	predict	0.001	0.000	10000	1000	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.257	0.682	See
3	KMeans_short	fit	0.146	0.005	10000	10000	2	full	random	30	300	1	0.0	30.0	NaN	30.0	NaN	0.074	0.005	1.984	0.151	See
4	KMeans_short	predict	0.000	0.000	10000	1	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.161	1.022	See
5	KMeans_short	predict	0.001	0.000	10000	1000	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.593	0.561	See
6	KMeans_short	fit	1.311	0.055	10000	10000	100	full	k-means++	30	300	1	0.0	21.0	NaN	21.0	NaN	0.610	0.036	2.150	0.157	See
7	KMeans_short	predict	0.001	0.000	10000	1	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.302	0.770	See
8	KMeans_short	predict	0.008	0.003	10000	1000	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	5.574	2.236	See
9	KMeans_short	fit	0.367	0.062	10000	10000	100	full	random	30	300	1	0.0	23.0	NaN	22.0	NaN	0.324	0.039	1.130	0.234	See
10	KMeans_short	predict	0.000	0.000	10000	1	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.076	1.076	See
11	KMeans_short	predict	0.004	0.002	10000	1000	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.002	0.001	1.729	1.084	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	30
n_init	1
tol	0.0

LogisticRegression vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	LogisticRegression	fit	15.740	0.096	1000000	1000000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	15.715	0.064	1.002	0.007	See
1	LogisticRegression	predict	0.000	0.000	1000000	1	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	0.318	0.286	See
2	LogisticRegression	predict	0.000	0.000	1000000	1000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	0.909	0.430	See
3	LogisticRegression	fit	1.205	0.016	1000	1000	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[26]	1.236	0.021	0.974	0.021	See
4	LogisticRegression	predict	0.000	0.000	1000	1	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.001	0.000	0.132	0.078	See
5	LogisticRegression	predict	0.002	0.000	1000	100	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.005	0.001	0.522	0.183	See

Plots

Shared hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

Ridge vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	Ridge	fit	2.675	0.040	100000	100000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.556	0.019	1.719	0.033	See
1	Ridge	predict	0.000	0.000	100000	1	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.253	0.180	See
2	Ridge	predict	0.001	0.000	100000	1000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.001	0.000	0.661	0.274	See
3	Ridge	fit	1.419	0.017	1000000	1000000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.332	0.005	4.276	0.084	See
4	Ridge	predict	0.000	0.000	1000000	1	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.352	0.310	See
5	Ridge	predict	0.000	0.000	1000000	1000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.591	0.328	See

Plots

Shared hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	False
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

Environment information

{
  "system_info": {
    "python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "0.24.1",
    "numpy": "1.20.2",
    "scipy": "1.6.2",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": null,
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.12.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.12",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}